Machine Learning – A New Wave of Intelligent Automation

“A year spent in artificial
intelligence is enough to make one believe in God.”

— Alan Perlis

In this day and age of digitalization,
humans and machines are living together. As far as we, i.e. humans, are
concerned, we have been evolving by learning from our past experience for millions
of years. On the flip side, however, the era of machine learning has just
started.

Since the arrival of avant-garde technology, the job of managing processes has become quite easy for humans. For instance, we can do shopping, banking, etc. from our comfort zone. Actually, it wouldn’t be wrong to say that living without the present state-of-the-art technology could be very difficult for us.

Expectations from machines have been increasing with every passing day. Many developed countries like USA, Australia, etc. have been working on artificial intelligence (AI) to make those things possible which were beyond our imaginations just a few decades ago.

With the help of AI, machines can learn from their own experience without being programmed for new things. This is known as Machine Learning (ML).

Machine learning, an application of artificial intelligence, lets systems learn from the stored data and give outcomes according to the situation. Seeking some real-time examples related to ML? Here’re some that may act as eye-openers:

While checking desired products on
e-commerce websites, generally, similar products of other brands also come out
as suggestions. Have you ever thought who decides those recommendations? Well, pal,
it is machine learning at work!

Another example: Sometimes, people get
contacted by banks or finance companies regarding a loan or an
insurance policy. But the question that arises here is ‘Do they contact all
their customers?’ Of course not! They select only those customers who seem
potential loaners or borrowers. In doing this, the underlying basis is none
other than machine learning algorithms.

Curious about the types of machine
learning algorithms? Let’s get the ball rolling:

Supervised Learning

You may already have got an idea about
supervised learning, as its name is self-explanatory. To leave nothing to
chance, however, we like to mention that these machines require a ‘supervisor’ to
learn. Here, a dataset acts as a teacher, and essays the role of a ‘trainer’ of
machines.

After the training, the output
generated by the machines gets checked against the intended result. If there’s
some difference, a process gets initiated to find errors. After identifying and
rectifying the ambiguities, the machines run again to check whether accurate results
are being generated or not.

Unsupervised Learning

Contrariwise to supervised
learning, these machines don’t need a teacher in order to learn. In
unsupervised learning, all that needs to be done is giving an initial protocol
and dataset to a machine. Once it’s done, the machine automatically starts
learning by creating clusters after finding out patterns and relationships in
the dataset.

Here, it is worthy to note that machines cannot add labels to the created cluster. For instance, they cannot say if an object belongs to ‘apples’ or ‘mangoes’ without prior classification; however, they can separate all the mangoes from the group of apples.

Semi-supervised learning

To describe semi-supervised
learning in the simplest manner, you can say it is the combination of supervised
and unsupervised learning. In supervised learning, a machine gets labeled data
to learn, while unlabeled data gets provided to a machine when unsupervised
learning is used.

By means of semi-supervised
learning, learning accuracy can be improved very easily. Customarily, semi-supervised
machine learning gets preference when the labeled data isn’t enough to train a
machine.

Reinforcement learning

Reinforcement learning, a type of
dynamic programming, trains a machine as per the system of reward and
punishment. For better understanding, if machines perform in the way which they
are supposed to, they get reward points and vice versa.

Actually, the idea behind this is
to make certain that machines act appropriately in the environment they are put
in. Furthermore, machines are deemed to swing into operation once they start
scoring maximum reward points and making negligible errors.

Wrapping up:

After understanding machine
learning’s basics, we hope that you have got a better insight into machine
learning (ML). Here, we would like to mention that many enterprises have been
using machine learning applications so that business growth can be achieved in
an effective manner.

There are many reputed vendors like
Amazon, Google, IBM, etc. that have immense experience in handling ML activities
that include data collection, data preparation, etc. For these activities,
they are being approached by those companies that really want to achieve their business
objectives as quickly as possible.

Are we done here? Nope. After
going through several research reports, we have got our hands on some stats
that have the potential to leave you stunned. So, take a gander:

85% of
customer interactions will be handled without any human intervention by 2020.

In 2017, Netflix
had saved $1 billion by using machine learning for making individualized recommendations.

20% of the
C-suites are already making use of machine learning.

72% of
business leaders say that AI lets humans focus on more productive work.

84% of organizations
believe that investing in AI can help gain significant competitive advantages.

Finally, we are finished with elaborating
upon the prominence of machine learning. Hope you have got all the information
that you were seeking, and enjoyed the whole tour of this write-up.